Metadata-Version: 2.1
Name: cloths_segmentation
Version: 0.0.2
Summary: High quality model for cloths segmentation.
Home-page: https://github.com/ternaus/cloths_segmentation
Author: Vladimir Iglovikov
License: MIT
Description: # cloths_segmentation
        Code for binary segmentation of cloths
        
        ## Data Preparation
        
        Download the dataset from [https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6)
        
        Process the data using script [https://github.com/ternaus/iglovikov_helper_functions/tree/master/iglovikov_helper_functions/data_processing/prepare_cloths_segmentation](https://github.com/ternaus/iglovikov_helper_functions/tree/master/iglovikov_helper_functions/data_processing/prepare_cloths_segmentation)
        
        The script will create process the data and store images to folder `images` and binary masks to folder `labels`.
        
        ## Training
        
        ### Define the config.
        Example at [cloths_segmentation/configs](cloths_segmentation/configs)
        
        You can enable / disable datasets that are used for training and validation.
        
        ### Define the environmental variable `IMAGE_PATH` that points to the folder with images.
        Example:
        ```bash
        export IMAGE_PATH=<path to the the folder with images>
        ```
        
        ### Define the environmental variable `LABEL_PATH` that points to the folder with masks.
        Example:
        ```bash
        export MASK_PATH=<path to the folder with masks>
        ```
        
        ### Training
        ```
        python -m cloths_segmentation.train -c <path to config>
        ```
        
        ### Inference
        
        ```bash
        python -m torch.distributed.launch --nproc_per_node=<num_gpu> cloths_segmentation/inference.py \
                                           -i <path to images> \
                                           -c <path to config> \
                                           -w <path to weights> \
                                           -o <output-path> \
                                           --fp16
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: test
